DOI QR코드

DOI QR Code

A Study on the Characteristics of Tourism Flow of Independent Tourists from China to South Korea Based on Tourists' Digital Footprint

디지털 여행기록 기반 중국 개별 관광객의 한국 관광경로 특성 분석

  • Wang, Chun-Yan (Faculty of Tourism Management, Jilin Engineering Normal University) ;
  • Jang, Phil-sik (Dept. of Air Transport and Logistics, Sehan University) ;
  • Kim, Hyung-Ho (Dept. of Air Transport and Logistics, Sehan University)
  • 왕춘염 (길림공정사범학원 관광관리학부) ;
  • 장필식 (세한대학교 항공교통물류학과) ;
  • 김형호 (세한대학교 항공교통물류학과)
  • Received : 2019.11.05
  • Accepted : 2020.01.20
  • Published : 2020.01.28

Abstract

This study takes Chinese independent tourists to South Korea as the research object, mines the data of tourists' digital footprints from online travel notes, and analyzes the characteristics of the tourism flow of Chinese independent tourists to South Korea by using the method of quantitative statistics and social network analysis(SNA). The results show that Seoul, Jeju Island, Busan and Daegu are the important tourist destinations for Chinese independent tourists entering South Korea. In addition, Qingdao, Tianjin, Shenyang, Hong Kong, Foshan and Macao are crucial hubs for Chinese independent tourists to visit South Korea. In future studies, the number of sample data should be increased. The time span of data collection should be extended for studying the annual variation characteristics of tourism flow and the trend of tourism hot spots.

본 연구에서는 한국을 방문한 개별 여행자들의 디지털 기록을 수집하여 정량적 통계 분석과 소셜 네트워크 분석(SNA)을 통해 한국을 방문하는 개별 중국 여행자들의 한국 관광 경로의 특징을 분석하였다. 연구결과 서울, 제주도, 부산 및 대구는 한국을 찾는 중국 여행자들의 주요 방문 장소이며, 중국의 청도, 천진, 심양, 홍콩, 포산 및 마카오는 한국을 찾는 중국 개별 여행자들의 주요 중계지 임을 알 수 있었다. 본 연구는 한국을 찾는 중국 개별 여행자들의 표본 특성과 각 여행 노드의 기능적 위치설정을 정확히 확인함으로써 정밀한 관광 마케팅과 관광 노선 개발에 활용할 수 있는데 의의가 있다. 향후 본 연구에 사용된 데이터의 추출 기간을 확대하고 더 많은 샘플을 확보하여 관광 경로의 연간 변동특성과 주요 방문지의 변동 특성을 분석할 필요가 있다.

Keywords

References

  1. A. V. Williams & W. Zelinsky. (1970). On some patterns in international tourism flows. Economic Geography, 46(4), 549-567. https://www.jstor.org/stable/142940 https://doi.org/10.2307/142940
  2. T. Hong, T. Ma & T. C. Huan. (2015). Network behavior as driving forces for tourism flows. Journal of Business Research, 68(1), 146-156. DOI : 10.1016/j.jbusres.2014.04.006
  3. Y. F. Li & H. Cao. (2018). Prediction for Tourism Flow based on LSTM Neural Network. Procedia Computer Science, 129, 277-283. DOI : 10.1016/j.procs.2018.03.076
  4. G. De Vita. (2014). The long-run impact of exchange rate regimes on international tourism flows. Tourism Management , 45, 226-233. DOI : 10.1016/j.tourman.2014.05.001
  5. R. Cellini & T. Cuccia. (2012). Museum and monument attendance and tourism flow: a time series analysis approach. Applied Economics, 45(24), 3473-3482. DOI : 10.1080/00036846.2012.716150
  6. Y. H. Hwang, U. Gretzel & D. R. Fesenmaier. (2006). Multicity trip patterns: Tourists to the United States. Annals of Tourism Research, 33(4), 1057-1078. DOI : 10.1016/j.annals.2006.04.004
  7. N. Scott, C. Cooper & R. Baggio. (2008). Destination networks: Four Australian cases. Annals of Tourism Research, 35(1), 169-188. DOI : 10.1016/j.annals.2007.07.004
  8. H. Y. Shih. (2006). Network characteristics of drive tourism destinations: An application of network analysis in tourism. Tourism Management, 27(5), 1029-1039. DOI : 10.1016/j.tourman.2005.08.002
  9. J. I. L. Miguens & J. F. F. Mendes. (2008). Travel and tourism: Into a complex network. Physica A Statistical Mechanics & Its Applications, 387(12), 2963-2971. DOI : 10.1016/j.physa.2008.01.058
  10. F. Girardin, F. Dal Fiore, C. Ratti, & J. Blat. (2008). Leveraging explicitly disclosed location information to understand tourist dynamics: A case study. Location Based Services , 2(1), 41-56. DOI : 10.1080/17489720802261138
  11. H. Kim & S. Stepchenkova. (2015). Effect of tourist photographs on attitudes towards destination: manifest and latent content. Tourism Management, 49, 29-41. DOI : 10.1016/j.tourman.2015.02.004
  12. S. Choi, X. Y. Lehto & A. M. Morrison. (2007). Destination image representation on the web: Content analysis of Macau travel related websites. Tourism Management, 28(1), 118-129. DOI : 10.1016/j.tourman.2006.03.002
  13. J. C. Garcia-Palomares, J. Gutierrez & C. Minguez. (2015). Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408-417. DOI : 10.1016/j.apgeog.2015.08.002
  14. M. H. Salas-Olmedo, B. Moya-Gomez, J. C. Garcia-Palomares & J. Gutierrez. (2018). Tourists' digital footprint in cities: Comparing Big Data sources. Tourism Management, 66, 13-25. DOI : 10.1016/j.tourman.2017.11.001
  15. C. Y. Wang, P. S. Jang & H. H. Kim. (2019). A Study on the Characteristics of the Seasonal Travel Path of Individual Chinese Travellers in Korea. Korea Convergence Society , 10(7), 23-31. DOI : 10.15207/JKCS.2019.10.7.023
  16. S. C. Song, T. H. Nguyen, S. H. Park & G. T. Yeo. (2018). Research Trends Analysis on Port Hinterland Using SNA Method. Journal of Digital Convergence, 16(11), 17-27. DOI : 10.14400/JDC.2018.16.11.017
  17. B. B. Delgado, H. M. Ma, J. G. Oh & G. T. Yeo. (2018). The Study on the research trend about Europe ports: focus on Baltic Sea using Keyword network. Journal of Digital Convergence, 16(2), 139-149. DOI : 10.14400/JDC.2018.16.2.139